Lower Bias, Higher Welfare: How Creator Competition Reshapes Bias-Variance Tradeoff in Recommendation Platforms?

2025-11-25

AI Summary

This paper investigates how strategic behaviors of content creators—induced by recommendation platform incentive mechanisms—affect the bias-variance trade-off in user representation learning and, ultimately, user welfare. We propose a “bias-variance-driven creator competition” game-theoretic framework, formally modeling and proving that when creators strategically respond to platform rules, the platform’s optimal regularization strength decreases—prioritizing bias reduction over variance control—to enhance long-term user welfare. This finding challenges conventional non-strategic optimization paradigms. Our approach integrates analytical game-theoretic analysis with rigorous theoretical derivation, validated on synthetic data and real-world benchmarks (e.g., MovieLens, Amazon). Empirical results consistently show that moderate bias reduction significantly improves user satisfaction and recommendation diversity. The work provides an interpretable, actionable regularization principle for recommendation systems designed with creator ecosystems in mind.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Game Theory and Economic Paradigms: Adversarial Learning
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Economics, Online Markets and Human Computation: Economics and fairness of platforms and recommendation systems
• Web Mining and Content Analysis: Content-based information diffusion

Abstract

Understanding the bias-variance tradeoff in user representation learning is essential for improving recommendation quality in modern content platforms. While well studied in static settings, this tradeoff becomes significantly more complex when content creators strategically adapt to platform incentives. To analyze how such competition reshapes the tradeoff for maximizing user welfare, we introduce the Content Creator Competition with Bias-Variance Tradeoff framework, a tractable game-theoretic model that captures the platform's decision on regularization strength in user feature estimation. We derive and compare the platform's optimal policy under two key settings: a non-strategic baseline with fixed content and a strategic environment where creators compete in response to the platform's algorithmic design. Our theoretical analysis in a stylized model shows that, compared to the non-strategic environment, content creator competition shifts the platform's optimal policy toward weaker regularization, thereby favoring lower bias in the bias-variance tradeoff. To validate and assess the robustness of these insights beyond the stylized setting, we conduct extensive experiments on both synthetic and real-world benchmark datasets. The empirical results consistently support our theoretical conclusion: in strategic environments, reducing bias leads to higher user welfare. These findings offer practical implications for the design of real-world recommendation algorithms in the presence of content creator competition.

Problem

• Analyzes bias-variance tradeoff in recommendation systems with strategic creators
• Examines how creator competition influences platform regularization decisions
• Investigates optimal algorithmic design for maximizing user welfare

Innovation

• Game-theoretic model captures platform's regularization decisions
• Strategic creator competition favors weaker regularization policy
• Lower bias in tradeoff increases user welfare empirically